• Phylogenomics is increasingly used to infer deep-branching relationships while revealing the complexity of evolutionary processes such as incomplete lineage sorting, hybridization/introgression and ...polyploidization. We investigate the deep-branching relationships among subfamilies of the Leguminosae (or Fabaceae), the third largest angiosperm family. Despite their ecological and economic importance, a robust phylogenetic framework for legumes based on genome-scale sequence data is lacking.
• We generated alignments of 72 chloroplast genes and 7621 homologous nuclear-encoded proteins, for 157 and 76 taxa, respectively. We analysed these with maximum likelihood, Bayesian inference, and a multispecies coalescent summary method, and evaluated support for alternative topologies across gene trees.
• We resolve the deepest divergences in the legume phylogeny despite lack of phylogenetic signal across all chloroplast genes and the majority of nuclear genes. Strongly supported conflict in the remainder of nuclear genes is suggestive of incomplete lineage sorting.
• All six subfamilies originated nearly simultaneously, suggesting that the prevailing view of some subfamilies as ‘basal’ or ‘early-diverging’ with respect to others should be abandoned, which has important implications for understanding the evolution of legume diversity and traits. Our study highlights the limits of phylogenetic resolution in relation to rapid successive speciation.
In 2000–2021, 150 various types of bridge structures were examined in different regions of Ukraine, where 742 individuals of 13 bat species were found: Myotis dasycneme, Myotis daubentonii, Myotis ...brandtii, Myotis mystacinus, Myotis aurascens, Myotis nattereri, Nyctalus noctula, Plecotus auritus, Plecotus austriacus, Pipistrellus pygmaeus, Pipistrellus nathusii, Pipistrellus kuhlii, and Eptesicus serotinus. The occupancy rate was 30.7%. M. daubentonii and P. auritus were observed in most of the studied regions. The frequency of observations of M. daubentonii was 50% of inhabited bridges (n = 23) and almost 65% of the number of individuals, with a concentration of localities in the western and north-western parts of the country. P. auritus (frequency 32.6%) dominated the bridges of the Dnipro Lowland and the Western Polissia. M. aurascens (Azov Sea region), M. mystacinus and M. brandtii (Western Polissia), P. austriacus (Black Sea region), and P. kuhlii (Azov Sea region) were found singly or only in some regions. Nursery colonies of M. daubentonii were found in the Western Polissia, Volynian–Podolian Upland, and Dnipro Lowland, N. noctula in the Dnipro region, P. auritus in the Western Polissia and the Black Sea region, and M. aurascens in the Azov Sea region. In a geographical aspect, the number of species in bridges is the highest in the regions of Western Polissia and Azov Sea (7 species each), and slightly lower in the Dnipro Lowland (n = 6). The highest percentage of inhabited bridges (64.3%) was found in the Dnipro Lowland; in other regions it was 37.8–10.5%. Bridges play an important role as summer roosts for bats (both for single individuals and breeding colonies), as well as in late summer–autumn (after the disbandment of breeding colonies) and as spring roosts (during seasonal migrations). Bridge structures can be important for the settlement of bats in regions with no underground cavities of natural or anthropogenic origin. We assume that this is the reason why the maximum number of species and abundance of bats in bridges was found in the Western Polissia and Dnipro Lowland, which are characterised by flat topography and lack of abundant and various underground cavities.
Stress corrosion cracking (SCC) initiation is usually simulated at the mesoscale, and these computations are usually expensive. This is made more computationally challenging or impossible when such ...simulations are coupled with a macroscale structural model required for reliability analysis, due to the sources of uncertainty from both scales. This paper tackles this computational barrier to perform physics-based corrosion reliability analysis of large structures using mesoscale simulations via a novel, adaptive surrogate modeling framework. A global surrogate model of the structure is first constructed from a finite element (FE) mechanical model to propagate various sources of input uncertainty at the macroscale to the local stress responses. After that, a mesoscale surrogate model is constructed from phase-field (PF) simulations to predict the failure probability of a given location by accounting for uncertainty in both the macroscale and mesoscale models. In order to guarantee the accuracy of the mesoscale surrogate model and reduce the number of PF simulations, an adaptive surrogate modeling method is proposed using importance sampling (IS) and active learning to refine iteratively the surrogate model in critical regions. Corrosion reliability analysis of a miter gate structure is adopted to demonstrate the efficacy of the proposed method. The result shows that the proposed framework can efficiently and accurately generate a failure probability map for a large structure like a miter gate based on computationally expensive mesoscale PF simulations. In addition, the proposed method is more accurate and converges faster than existing surrogate model-based reliability analysis algorithms.
Conventionally, event-related potential (ERP) analysis relies on the researcher to identify the sensors and time points where an effect is expected. However, this approach is prone to bias and may ...limit the ability to detect unexpected effects or to investigate the full range of the electroencephalography (EEG) signal. Data-driven approaches circumvent this limitation, however, the multiple comparison problem and the statistical correction thereof affect both the sensitivity and specificity of the analysis. In this study, we present SHERPA - a novel approach based on explainable artificial intelligence (XAI) designed to provide the researcher with a straightforward and objective method to find relevant latency ranges and electrodes. SHERPA is comprised of a convolutional neural network (CNN) for classifying the conditions of the experiment and SHapley Additive exPlanations (SHAP) as a post hoc explainer to identify the important temporal and spatial features. A classical EEG face perception experiment is employed to validate the approach by comparing it to the established researcher- and data-driven approaches. Likewise, SHERPA identified an occipital cluster close to the temporal coordinates for the N170 effect expected. Most importantly, SHERPA allows quantifying the relevance of an ERP for a psychological mechanism by calculating an "importance score". Hence, SHERPA suggests the presence of a negative selection process at the early and later stages of processing. In conclusion, our new method not only offers an analysis approach suitable in situations with limited prior knowledge of the effect in question but also an increased sensitivity capable of distinguishing neural processes with high precision.
Nanofiltration (NF) plays an increasingly central role in water/salts separation, which puts forward tailored requirements on NF membranes in a variety of application scenarios. However, the ...ambiguous separation mechanisms of NF including membrane structure parameters and operating conditions hinder the rational design of versatile NF membranes. Herein, machine learning was used to explore the correlation between membrane structure parameters and operating conditions with water/salts selectivity, and reveal the importance of the diverse features based on literature data. Two structural features of polyamide NF membrane (pore radius and zeta potential) and two operating parameters (pressure and feed concentration) were typically extracted and associated with water/salts selectivity. Random Forest and XGBoost models were employed to learn from relevant variables and assess their importance. The results showed that membrane structure parameters attached more importance to water/salts selectivity than that of operating conditions accompanying the variable influence for different typed salts, where symmetrical salts were mainly governed by size sieving while Donnan exclusion for asymmetrical salts. Structure-performance relationships between pore radius, zeta potential and diverse water/salts selectivity were established using partial dependence plot analysis. It is anticipated that the constructed comprehensive insights can be further leveraged to tackle performance modulation and oriented design of multi-scenarios NF membranes.
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•Machine learning was used to understand NF separation mechanisms.•Random Forest and XGBoost models were employed to learn from relevant variables and assess features importance.•Symmetrical salts were mainly governed by size sieving while Donnan exclusion for asymmetrical salts.•The integrated management of pore size and zeta potential for the oriented design of versatile NF membranes was highlighted.
Purpose: This study assessed the congruency of a graduate physical education program's goals and objectives using the Importance-Performance Analysis (IPA) framework. The research aimed to identify ...strengths, weaknesses, and opportunities for improvement, ultimately enhancing curriculum and bridging the gap between academia and the physical education and sports industry.
Materials and Methods: a cross-sectional survey was conducted, utilizing a structured questionnaire distributed to 135 participants classified as internal and external stakeholders selected via purposive convenience sampling. The questionnaire was administered via Google Forms. The questionnaire consisted of items related to stakeholders' demographic profile, awareness, acceptability, and perceived levels of importance and performance of the goals and objectives of the program. Data analysis involved descriptive statistics, IPA, and Independent Samples T-test to determine varying perceptions among stakeholders.
Results: The findings indicated a substantial degree of awareness and acceptability of the program's goals and objectives among stakeholders. The study also revealed a strong alignment between the program objectives, graduate school objectives, and the university's vision and mission. However, certain discrepancies emerged concerning stakeholders' acceptability levels for specific objectives. With regard to the importance-performance analysis, the results highlighted the need for the organization to enhance its performance in producing graduates possessing advanced disciplinary content knowledge. Graduates should be equipped to examine and appreciate both traditional and contemporary theories, concepts, and models within their respective disciplines.
Conclusion: The study concludes that stakeholders have high awareness and acceptability of the preambular provisions of the university and the MPE program, but external stakeholders have lower awareness and acceptability. The congruency between program objectives and graduate school objectives, as well as the alignment between the university vision and graduate school vision and mission, suggests a well-designed and implemented program. Meanwhile, the importance-performance analysis highlights areas to improve, such as advanced disciplinary content, effective communication, and critical thinking and research skills. Enhancing communication and engagement strategies, monitoring alignment with stakeholder needs, and reassessing the importance of certain goals can improve the program's alignment with broader institutional goals and contribute to the university's overall success.
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